How to effectively test if gdrcopy is enabled using Real world ML workload ?
Nobody has claimed this yet.
Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 25/100
- Issue type
- Documentation
- Clarity
- Needs clarification
- Activity status
- Stale
- Tech stack
- c, linux
- Domain
- machine-learning, performance, testing-qa
Research direction
No source file is named. Start with the gdrcopy tests already completed and the containerized language-model workload, then identify observable profiling output for the model-loading path. Done means documenting a reproducible way to distinguish gdrcopy activity from ordinary loading and stating what evidence confirms it is enabled.
Written by the indexing model from the issue text.
Description
I have successfully installed gdrcopy on my host and completed its tests. Afterwards, I launched a container running my language model application, with a focus on profiling the loading of the model from the local disk. I am looking for methods to confirm whether gdrcopy is active when my application is running. Since I am new to this, I would appreciate any guidance on how to verify the operation of gdrcopy in this context.
- Dominant language
- C
- Stars
- 1.4k
- Forks
- 198
- PR merge metrics
- No merged PRs in 30d
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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